The field of medical imaging has seen significant advances since the time X-Rays were first used to determine anatomical abnormalities. Medical imaging hardware has progressed in the form of newer machines such as Medical Resonance Imaging (MRI) scanners, Computed Axial Tomography (CAT) scanners, etc. Because of large amount of image data generated by such modern medical scanners, there has been and remains a need for developing image processing techniques that can automate some or all of the processes to determine the presence of anatomical abnormalities in scanned medical images.
Recognizing anatomical structures within digitized medical images presents multiple challenges. For example, a first concern relates to the accuracy of recognition of anatomical structures within an image. A second area of concern is the speed of recognition. Because medical images are an aid for a doctor to diagnose a disease or condition, the speed with which an image can be processed and structures within that image recognized can be of the utmost importance to the doctor reaching an early diagnosis. Hence, there is a need for improving recognition techniques that provide accurate and fast recognition of anatomical structures and possible abnormalities in medical images.
Digital medical images are constructed using raw image data obtained from a scanner, for example, a CAT scanner, MRI, etc. Digital medical images are typically either a two-dimensional (“2-D”) image made of pixel elements or a three-dimensional (“3-D”) image made of volume elements (“voxels”). Such 2-D or 3-D images are processed using medical image recognition techniques to determine the presence of anatomical structures such as cysts, tumors, polyps, etc. Given the amount of image data generated by any given image scan; it is preferable that an automatic technique should point out anatomical features in the selected regions of an image to a doctor for further diagnosis of any disease or condition.
One general method of automatic image processing employs feature based recognition techniques to determine the presence of anatomical structures in medical images. However, feature based recognition techniques can suffer from accuracy problems.
Automatic image processing and recognition of structures within a medical image is generally referred to as Computer-Aided Detection (CAD). A CAD system can process medical images and identify anatomical structures including possible abnormalities for further review. Such possible abnormalities are often called candidates and are considered to be generated by the CAD system based upon the medical images.
There are numerous reasons that both accuracy and speed of acquisition of image data need to be increased. For example, when using X-rays or CT imaging, it is desirable to minimize the time and area of exposure to potentially harmful radiation. By way of example, computed tomography (CT) imaging is the practice of visualizing the internal structure of a subject using a series of x-rays taken at multiple angles, the data from which may be combined and rendered by a computer system for illustrating the internal structure of the subject in three-dimensions. While CT imaging is relatively safe, it does involve exposure to ionizing radiation, which could become harmful in patients. Accordingly, it is generally considered prudent to limit the acquisition of image data to a particular field of the subject's body. By scanning only this field, the patient's exposure to ionizing radiation can be limited and the time needed to acquire the image reduced. Moreover, by limiting the scanning field, it is possible to acquire the desired image data more quickly and with less use of resources than if the entire body was scanned.
It is therefore important to be able to correctly identify the scanning field so that the resulting CT image captures the desired structural data. If the scanning field is selected to be sufficiently large, then there is less risk of missing pertinent structural data. However, the more precise the field is, the faster the scan can be performed and the less the subject is exposed to potentially harmful ionizing radiation. Accordingly, it is desirable to select a precise scanning field that is only as large as is necessary to capture the desired structural data.
In order to set the scanning field, often the CT scanner is used to produce one or more topograms of the subject's body. A topogram is a scout image that may be used to establish where the target organs are located within the subject's body so that the scanning field may be precisely selected. The topogram appears similar to a conventional radiograph, where the outline of the subject's body may be seen with certain organs and anatomical features superimposed thereon.
Presently, the scanning field is manually determined by a human operator such as a radiology technician. The human operator uses learned knowledge of human anatomy to identify the organs to be imaged and then selects the scanning field to be scanned in detail. However, this manual determination may take an amount of time that is noticeable to the subject, and as such, there is a greater possibility that the subject may shift position between the acquisition of the topogram and the acquisition of the CT scan within the manually determined scanning field. Accordingly, the manually determined scanning field must be selected with wide margins to allow for subtle movement. Moreover, the manually selected scanning field may be slightly different each time a CT scan is performed and thus multiple CT scans, such as follow-up studies of the same patient and/or cross-patient comparisons, may be more difficult to compare owing to the inherent inconsistency of the manual field selection. The description of CT image capture and its limitations is merely exemplary, as similar issues surround the use of other imaging modalities as well.
The amount of medical image data produced is constantly growing. In addition to the above-described difficulties in correctly identifying a scanning field for a medical image study, annotation of the ever-increasing number of medical images is an overwhelming task. Manually annotating these images is costly and error-prone, which means that automatic annotation algorithms are needed and must to be able to perform the task reliably and efficiently. This is particularly true for radiograph images, although similar issues exist for other imaging modalities.
A great challenge for automatic medical image annotation is the large visual variability across patients in medical images from the same anatomy category. In some cases, diseases or artifacts can render an anatomy unrecognizable even by human eyes. Additionally, an automatic annotation system must be able to automatically recognize the projection view of for example, chest radiographs.
Therefore there is a need for improved systems and methods to facilitate robust anatomy detection in medical images, and systems and methods for automatically annotating medical images such as radiograph images.